Individualized Machine-learning-based Clinical Assessment Recommendation System.

ABSTRACT:

Background

Traditional clinical assessments often lack individualization, relying on standardized procedures that may not accommodate the diverse needs of patients, especially in early stages where personalized diagnosis could offer significant benefits. We aim to provide a machine-learning framework that addresses the individualized feature addition problem and enhances diagnostic accuracy for clinical assessments.

Methods

Individualized Clinical Assessment Recommendation System (iCARE) employs locally weighted logistic regression and Shapley Additive Explanations (SHAP) value analysis to tailor feature selection to individual patient characteristics. Evaluations were conducted on synthetic and real-world datasets, including early-stage diabetes risk prediction and heart fa

SUBMITTER: Setiawan D 

PROVIDER: S-EPMC11302612 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature